Crashworthiness design methodology using a hybrid cellular automata algorithm for the synthesis of topologies for structures subject to nonlinear transient loading

ABSTRACT

Crashworthiness designing of a structure using a Hybrid Cellular Automata (HCA) algorithm where field states are computed using finite element analysis (FEA) and the material distribution of the structure is updated at each iteration using cellular automata method. The HCA algorithm optimizes the topology of the structures to achieve certain performance within the limits of various constraints applied to ensure crashworthiness of the structures. The HCA algorithm may also be applied to designing of structures to be fabricated by an extrusion method having the same cross section along the direction of extrusion or stamped structures having thickness varying across the structure.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(e) to co-pending U.S. Provisional Patent Application No. 61/016,311 entitled “A Crashworthiness Design Methodology Using a Hybrid Cellular Automata Algorithm for the Synthesis of Topologies for Structures Subject to Nonlinear Transient Loading” filed on Dec. 21, 2007, which is incorporated by reference herein in its entirety.

GOVERNMENT FUNDING

This invention was made with government support under Grant #1R49CE000789-01 awarded by Center for Disease Control and Prevention (CDC). The government has certain rights in the invention.

FIELD OF THE INVENTION

This disclosure is related to a method and system for designing structures subject to nonlinear transient loads, more specifically to a method and system for crashworthiness designing structures using Hybrid Cellular Automata (HCA) algorithm.

BACKGROUND OF THE INVENTION

Crashworthiness design methodologies combine crash simulation and design methodology in order to design structures in vehicles. A crashworthiness design requires the modeling of complex interactions between components of the vehicles in a crash event. The design of the structures must also accommodate various conflicting criteria such as limiting the maximum acceleration to avoid injuries to and limiting the maximum deformation to avoid injuries attributable to deformed structures penetrating a passenger cabin. Further, the crashworthiness design often requires designing the structures so that the structures absorb as much energy as possible while deforming in a manner that does not cause injury the passengers.

Most commercial software products for designing structures assume that only elastic deformation takes place in the structures. Further, it is assumed that a load is applied to the structures over a long period of time, which results in a steady-state response from the structures. These assumptions, however, become unrealistic for crash events where loads to the structure and boundary conditions change as time progresses. Accordingly, most commercial software products are incapable of performing crashworthiness designing.

Gradient-based method is one of the techniques used in the crashworthiness design. The gradient-based method, however, requires a nonlinear dynamic finite element analysis that is computationally intensive. Therefore, the gradient-based method takes a very long time to design and evaluate the structures. One way to reduce the computation is to simplify the finite element analysis (FEA). But simplifying the finite element analysis will sacrifice the accuracy and reliability of the crashworthiness design.

SUMMARY OF THE INVENTION

Embodiments provide a method, a device and a computer readable storage medium for designing a structure representing a physical object by an optimization process using a Hybrid Cellular Automata (HCA) algorithm where the structure is segmented into a plurality of cells, analyzed by a finite element analysis (FEA), and updated by cellular automata computation. A design variable representing a material distribution in each cell is updated by a material distribution update rule to converge the field state of each cell towards a target field state of each cell. The process of analyzing the structure by the FEA and updating by the cellular automata computation is repeated until a termination condition is satisfied.

In one embodiment, a performance variable representing a property of the structure required to satisfy a performance constraint is calculated. The performance constraint may be associated with forces on the structure and displacements or deformation of the structure. The performance variable may be any property of the structure that is associated with the performance constraint.

In one embodiment, the design variable is a material density of the cell or a thickness of the cell. The material density or the thickness of the cell is updated by the cellular automata based on the field state of the cell to evolve the initial structure into an optimized structure.

In one embodiment, the field state of the cell is the internal energy density of the cell responsive to applying a load to the structure. Crashworthiness designing of a structure involves distributing the internal energy density across the cells as evenly as possible. By obtaining the design of the structure with an even distribution of the internal energy density, all of the cells contribute equally to the absorption of kinetic energy during a crash event. The even distribution of the internal energy density across the cells, therefore, may result in a more crashworthy design of the structure.

In one embodiment, the HCA algorithm is applied to designing a stamped structure with uneven cell thickness. The thickness is employed as a design variable, and is updated by the cellular automata computation.

The features and advantages described in the specification are not all inclusive and, in particular, many additional features and advantages will be apparent to one of ordinary skill in the art in view of the drawings, specification, and claims. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of this disclosure can be readily understood by considering the following detailed description in conjunction with the accompanying drawings.

FIG. 1 is a block diagram illustrating a structure designer, according to one embodiment.

FIG. 2 is a block diagram illustrating a block diagram of a memory in the structure designer of FIG. 1, according to one embodiment.

FIG. 3 is a flow chart illustrating a method of designing a structure using a Hybrid Cellular Automata (HCA) algorithm, according to one embodiment.

FIG. 4 is a graph illustrating weights for computing a current field state, according to one embodiment.

FIG. 5 is a flow chart illustrating a method of designing a structure subject to performance constraints, according to one embodiment.

FIG. 6 is a diagram illustrating stress-strain curves for varying densities of material, according to one embodiment.

FIG. 7 is a diagram illustrating a discretized three-dimensional structure for fabrication by an extrusion method, according to one embodiment.

FIG. 8 is a flow chart illustrating a method of designing a structure for fabrication by an extrusion method, according to one embodiment.

FIG. 9 is a diagram illustrating the distribution of an internal energy density in a column of the structure of FIG. 7, according to one embodiment.

DETAILED DESCRIPTION OF THE INVENTION

A preferred embodiment of the present invention is now described with reference to the figures where like reference numbers indicate identical or functionally similar elements.

Reference in the specification to “one embodiment” or to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

Some portions of the detailed description that follow are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps (instructions) leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic or optical signals capable of being stored, transferred, combined, compared and otherwise manipulated. It is convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. Furthermore, it is also convenient at times, to refer to certain arrangements of steps requiring physical manipulations of physical quantities as modules or code devices, without loss of generality.

However, all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or “determining” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Certain aspects of the present invention include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the present invention could be embodied in software, firmware or hardware, and when embodied in software, could be downloaded to reside on and be operated from different platforms used by a variety of operating systems.

The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.

The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any references below to specific languages are provided for disclosure of enablement and best mode of the present invention.

In addition, the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

Embodiments provide a method, a device and a computer readable storage medium for crashworthiness designing of a structure using a Hybrid Cellular Automata (HCA) algorithm where field states are computed using finite element analysis (FEA) and the material distribution of the structure is updated at each iteration using cellular automata method. The HCA algorithm optimizes the topology of the structures to achieve a target performance within the limits of various constraints applied to ensure crashworthiness of the structures. The HCA algorithm may also be applied to designing of structures to be fabricated by an extrusion method having the same cross section along the direction of extrusion or stamped structures having thickness varying across the structure.

The structures that can be designed using the embodiments include, among others, parts in various types of vehicles (e.g., automobiles, aircrafts, ships), construction elements, consumer products, furniture, construction machines, and safety equipments.

Architecture of Structure Designer

FIG. 1 is a block diagram illustrating a structure designer 110, according to one embodiment. The structure designer 110 may be a general purpose computer or a dedicated computer structured to design and evaluate structures using the method disclosed herein. In the embodiment of FIG. 1, the structure designer 110 includes, among other components, a processor 112, a memory 114, an input module 116, a display module 118, and a bus 130 connecting these components. The structure designer 110 may include other components that are omitted herein to avoid obfuscation of the embodiment.

The processor 112 executes instructions stored in the memory 114 to generate the designs of structures and optimize the topologies of the structures. The processor 112 may comprise various computing architectures such as a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets. Although only a single processor is shown in FIG. 1, multiple processors may be included. The processor 112 may comprise an arithmetic logic unit, a microprocessor, a general purpose computer, or some other information appliance equipped to transmit, receive and process data signals from the memory 114.

The memory 114 stores various software components including, among others, a structure design storage 210, a cell divider 220, a finite element analysis (FEA) engine 230, and a cellular automata engine 240. The structure design storage 210 stores the parameters of the structures as they are generated in each iteration of the HCA algorithm processing. The parameters of the designed structures may be stored in the structure design storage 210 for display on the display module 118 or output via other output devices of the structure designer 110. One or more of the components in the memory 114 may be embodied in hardware, software, firmware or any combinations thereof.

The cell divider 220 reads the data of the initial structure from the structure design storage 210 and segments the structure into multiple cells for processing by the FEA engine 230 and the cellular automata engine 240. Specifically, the cells may have uniform size or have non-uniform size. In one embodiment, the FEA engine 230 and the cellular automata engine 240 use the same segmentation or configuration of cells. In another embodiment, the FEA engine 230 and the cellular automata engine 240 use different segmentation or configuration of cells.

The FEA engine 230, in conjunction with the cellular automata engine 240, performs the HCA algorithm. The FEA engine 230 performs numerical analysis on structures by dividing the structure in the current iteration of the HCA algorithm into cells (finite elements), and then computes equations representing the equilibrium of the cells to approximate one or more field states S (e.g., stress, strain and internal energy density) associated with the structure. In one embodiment, LS-Dyna available from Livermore Software Technology Corporation (LSTC) at Livermore, Calif. is used as the FEA engine 230.

The cellular automata engine 240 updates a design variable x representing a material distribution for a cell in the structure based on a material distribution rule that takes into account the field state of neighborhood cells. The state α_(i) ^((k)) of ith cell in a kth iteration of the HCA algorithm may be expressed by the following equation:

$\begin{matrix} {\alpha_{i}^{(k)} = \begin{Bmatrix} S_{i}^{(k)} \\ x_{i}^{(k)} \end{Bmatrix}} & {{Equation}\mspace{14mu} (1)} \end{matrix}$

where S_(i) ^((k)) represents a set of field states, and x_(i) represents the design variable of ith cell in the structure. The basic idea of the HCA algorithm is to compute the field state S_(i) ^((k)) using the FEA engine 230 while updating the design variable x_(i) using the cellular automata engine 240. The HCA algorithm differs from a general cellular automata algorithm in that the information of the field state S_(i) ^((k)) is calculated from a global analysis (e.g., finite element analysis) and then used to adjust the design variable x_(i).

In one embodiment, the design variable x_(i) for ith cell represents one of the following material properties associated with the structure: density (ρ), elastic modulus (E), yield stress (σ_(Y)), strain-hardening, and modulus (E_(h)). The design variable x_(i) may be scaled to have a value not less than 0 and not larger than 1 (0≦x_(i)≦1). In this example, x_(i)=0 means that ith cell is a void whereas x_(i)=1 means that the cell is full of material. Any values between 0 and 1 means that the cell is partly filled with the material. In another embodiment, the design variable x_(i) for ith cell represents the thickness of the cell, as described below in detail with reference to stamped structures.

Method of Designing a Structure Using HCA Algorithm

FIG. 3 is a flow chart illustrating a method of designing a structure, according to one embodiment. First, an initial design of the structure is loaded 310 into the cell divider 220. The structure is then segmented 316 into a plurality of cells by the cell divider 220. The information about the structure and its segmentation is then fed into the FEA engine 230 to perform 320 a conventional structural analysis. In one embodiment, transient nonlinear finite element analysis is used to perform the structural analysis. The FEA engine 230 determines, among others, the field states S_(i) ^((k)) as a result of the structural analysis.

The result of the structural analysis is then provided to the cellular automata engine 240 together with information about the structure and its segmentation of the cells. The cellular automata engine 240 then updates 330 the material distribution of the structure according to a material distribution rule. Specifically, the design variable x_(i) of each cell in the structure is updated according to the material distribution rule. In one embodiment, the distribution of material in the structure is updated to evolve the initial design of the structure over multiple iterations into a final structure with cells have approximately the same internal energy density. By obtaining the design of the structure with even distribution of the internal energy density, all of the cells contribute equally to absorption of kinetic energy during a crash event. The even distribution of the internal energy density across the cells, therefore, may result in a more crashworthy design of the structure.

In one embodiment, proportional, integral and derivative (PID) feedback is used to reduce the oscillatory behavior of the design variable x_(i) from one iteration of the HCA algorithm to the next. The change in the design variable x_(i) of ith cell at kth iteration of the HCA algorithm using the PID feedback may be expressed in the following equation:

Δx _(i) ^((k)) =K _(p) e _(i) ^((k)) +K _(i)∫_(k−2) ^(k) e _(i) ^((k)) dκ+K _(d) Δe _(i) ^((k))  equation (2)

where K_(p) is a proportional control gain, K_(i) is an integral control gain, K_(d) is the derivative gain, e_(i) ^((k)) is the error between the average field state of the cell S _(i) ^((k)) and a target field state of the cell S*_(i) ^((k)). The error e_(i) ^((k)) in equation (2) can be expressed as:

e _(i) ^((k)) = S _(i) ^((k)) −S* _(i) ^((k))  equation (4)

The target field state refers to the field state attempted to be reached in the current iteration. The target field state may be changed at each iteration based on, for example, the field states of the cells in the structure. The target field stat is caculated based on a target fractional mass provided by a user.

Although the integral error in equation (2) is limited to the error from the previous two iterations, the number of iterations for the integral error may be increased or decreased.

In one embodiment, equation (3) is simplified by setting K_(i) and K_(d) to zero, and combining equation (4). The resulting equation is as follows:

Δx _(i) ^((k)) =K _(p)( S _(i) ^((k)) −S* _(i) ^((k)))  equation (5)

The embodiments are described below using equation (5) for the sake of explanation.

In one embodiment, the field state of previous iterations may be used in conjunction with equation (5) to reduce oscillation between iterations. For example, the field state S_(i) ^((k)) of each cell may be represented by an exponential moving average of the internal energy density at the current iteration and n previous iterations, for example, as expressed in the following equation:

$\begin{matrix} {S_{i}^{(k)} = {\frac{1}{\sum\limits_{k = 0}^{n}w_{i}^{k}}{\sum\limits_{j = 0}^{n}{w^{j}U_{i}^{({k - j})}}}}} & {{equation}\mspace{14mu} (6)} \end{matrix}$

where U_(i) represents internal energy density of the ith cell, w_(i) represents weights given to the internal energy density computed at the current and previous iterations. Equation (6) takes into account the field states in previous iterations of the HCA algorithm and thereby gives each cell a memory of its previous states. The weights are expressed in the following equation:

w _(i)=(x _(i) −x _(min))²  equation (7)

where x_(min) indicates the minimum density allowed. Ideally the x_(min) is zero but this value is fixed to a small value (e.g., 0.05) to avoid computational issues. FIG. 4 is a graph illustrating the relative weights

$\frac{w^{j}}{\sum\limits_{k = 0}^{4}w_{i}^{k}}$

given to the internal energy of ith cell at current (j=0) and previous iterations (j=1 to j=3) when n is set to 4.

After updating the distribution of material for all cells, the process proceeds to check whether a predetermined termination criteria is satisfied 350. For example, the convergence of parameters in the design of the structure may be checked. That is, the termination criteria include the difference between a parameter in current iteration and a parameter in a next iteration falling below a threshold. If the criteria are not satisfied, the process returns to the step 320 of performing the structural analysis of the structure using the FEA engine 320. If the criteria are satisfied, the final design is output 360 from the structure designer 110.

In one embodiment, the HCA algorithm designs structures within the constraints of the total mass of the structure. The total mass of the structure is correlated with the production cost of the structure. Therefore, the total mass of the structure may be constrained to generate the design of the structure that satisfies cost constraints. The internal energy stored in the structure, however, is directly correlated with the mass of the structure. The higher the target internal energy, the lower the mass of the structure and vice versa. Therefore, to determine an acceptable final mass of the structure, a target internal energy must be determined.

One way of finding this target internal energy is to iterate the HCA algorithm and adjust the target internal energy until the mass constraint is satisfied. The target internal energy for the (k+1)th iteration of the HCA algorithm can be expressed as:

$\begin{matrix} {S^{*{({j + 1})}} = {S^{*{(j)}}\left( \frac{M_{f}^{({k + 1})}}{M_{f}^{*}} \right)}} & {{equation}\mspace{14mu} (8)} \end{matrix}$

where M*_(f) is a target fractional mass. The initial target field state S*⁽⁰⁾ is the average field state of all cells in the kth iteration. The fractional mass M_(f) of the structure is defined as follows:

$\begin{matrix} {M_{f} = \frac{\sum\limits_{i = 1}^{N}x_{i}}{\sum\limits_{i = 1}^{N}1}} & {{equation}\mspace{14mu} (9)} \end{matrix}$

where N represents the total number of cells in the structure. The fractional mass M^(f) indicates the fraction of mass relative to the mass of initial design having full density cells and is set by the user. The fractional mass M_(f) is proportional to the actual mass of the structure. When the fractional mass M_(f) of the structure at the kth iteration of the HCA algorithm satisfies the target mass M*_(f) of the structure, the HCA algorithm is terminated. In this way, the final design of the structure satisfies the mass constraint.

Crashworthiness Design Based on Performance Constraints

Crashworthiness design of a structure may be limited by one or more factors. For example, the crashworthiness design may involve limiting of the displacement of the structure, reaction forces of the structure or the strain in the structure. If the displacement of the structure is excessive, passengers may experience acceleration that may injure the passengers or result in failure or buckling of the structure during the collusion. The reaction forces generated during the collision may also need to be constrained because high reaction forces may cause permanent damage to a vehicle. The strain may also need to be limited because excessive strain will cause the material to crack or fail and no longer withstand the load. The HCA algorithm may be applied to generate the designs of structures satisfying such predetermined performance constraints.

FIG. 5 is a flow chart illustrating a method of designing a structure to satisfy the predetermined performance constraints, according to one embodiment. The steps 510, 516, 520, 530, 550 and 560 in FIG. 5 are substantially the same as the steps 310, 316, 320, 530, 550 and 560 in FIG. 3, respectively. Therefore, the description of these steps is omitted herein. The only step added in FIG. 5 compared to FIG. 3 is the step 524 of updating the target fractional mass. Specifically, by updating the target fractional mass, the performance constraints of the structure may be adjusted while achieving a target optimization objective (e.g., even distribution of the internal energy density across the structure). In the following, the structures are designed to satisfy one of the three performance constraints: (i) a maximum displacement constraint, (ii) a maximum reaction force constraint, and (iii) a maximum strain constraint.

In one embodiment, the maximum displacement constraint is imposed as a performance constraint on the design of the structure. In this embodiment, a control-based strategy is used to determine the amount of change in the target fractional mass. Specifically, an error e_(DC) is defined as follows:

$\begin{matrix} {e_{D\; C} = \frac{d_{m\; {ax}} - d_{m\; {ax}}^{*}}{d_{m\; {ax}}^{*}}} & {{equation}\mspace{14mu} (10)} \end{matrix}$

where d_(max) is the maximum displacement of the structure, and d*_(max) is a target displacement set to satisfy design requirements. The reduction of the target fractional mass in step 524 in current iteration of the HCA algorithm is expressed as follows:

ΔM* _(f)=(K _(DC) x )e _(DC)  equation (11)

where K_(DC) is a proportionality constant for displacement constraint, and x is a density scaling parameter indicating the portions of cells that is either void of filled as described below in detail.

The global stiffness of the structure in terms of maximum displacement is difficult to determine when the cells are partly filled with material (that is, 0<x_(i)<1). Therefore, in one embodiment, larger changes in the fractional mass are allowed as the design converges using the HCA algorithm, and each cell moves toward being a void (x_(i)=0) or being fully filled with material (x_(i)=1). The scaling parameter x is defined as follows:

$\begin{matrix} {\overset{\_}{x} = \frac{{{\sum\limits_{i = 1}^{\overset{\_}{n}}x}}_{x > x_{m\; i\; n}}}{\overset{\_}{n}}} & {{equation}\mspace{14mu} (12)} \end{matrix}$

where n represents the number of cells that have design variable x_(i) greater than x_(min).

Although the maximum displacement and the target fractional mass have approximately quadratic relationship for a structure where most of its cells have the design variable x_(i) of 0 or 1, a linear relationship is approximated for small changes in the fractional mass during the updating of the target fractional mass (e.g., ΔM*_(f)=0.1) because the maximum allowable change of the design variable Δx_(max) is 0.1 for numerical stability in the FEA. By defining the reduction of the target fractional mass as a function of the scaling parameter x that indicates the portion of cells in the structure that is either void of fully filled, an efficient convergence to optimized design of the structure can be achieved. As the iteration of the HCA algorithm proceeds and approaches the final structure exhibiting the desired force-displacement behavior, the permitted change in the target fractional mass may be increased. By changing the target fractional mass (and hence the total mass), the force and displacement constraints can be accommodated.

In one embodiment, the maximum reaction force constraint is imposed as a performance constraint on the design of the structure. The reaction force generated during a contact between two structures is also directly correlated with the mass of the structure and the global stiffness of the structure. Therefore, the methodology for controlling the reaction force is again based on finding the appropriate mass of the structure. Similar to the embodiment of imposing the maximum displacement constraint as set forth above, the reaction force constraint is applied globally by penalizing the amount of material needed for the structure. Specifically, the threshold mass of the structure is lowered until the maximum reaction force is reached. A control-based strategy is also used to adjust the target fractional mass M*_(f) to control the maximum reaction force in the structure.

An error for maximum reaction force e_(FC) is quantified by integrating the normalized error between the actual force-displacement F_(r)(d) of the structure and a target set of forces applied to the structure F_(r)*(d) as expressed by the following equation:

$\begin{matrix} {e_{FC} = {\int_{d_{i}}^{d_{f}}{\left( \frac{{F_{r}(d)} - {F_{r}^{*}(d)}}{F_{r}^{*}(d)} \right){d}}}} & {{equation}\mspace{14mu} (13)} \end{matrix}$

where d_(i) is the initial displacement of the structure and d_(f) is the final displacement of the structure. The reduction of the target fractional mass in step 524 in current iteration of the HCA algorithm is expressed as follows:

ΔM* _(f)=(K _(FC) x )e _(FC)  equation (14)

where KFC is a scale parameter employed to improve convergence, and x is a scale parameter that indicates the portion of cells that is either void of filled.

In one embodiment, the maximum strain constraint is imposed as a performance constraint on the design of the structure. The structure designer 110 may generate the design of a structure that maximizes the energy absorption of the structure while not exceeding a strain level in any parts of the structure during a collision. If a cell is determined as having largest strain that exceeds predetermine maximum strain, the mass of the material in the cell is increased. By increasing the material in the cell, the overall level of strain in the cells is decreased.

FIG. 6 is a diagram illustrating stress-strain curves for varying design variables x_(i) (relative density in this case) of the material in a cell, according to one embodiment. At kth iteration of the HCA algorithm, the density variable is x_(i)* and the maximum strain in the ith cell is determined as E_(K). By increasing the design variable from x_(i) ^(k)=x*_(i)* to x_(i) ^(k+1)=x_(i)*+Δx, the maximum strain in the ith cell is reduced to a target strain E_(k+1)=E*. Vx may be determined by computing a design variable x_(i) ^(k+1) where the amount of additional internal energy (represented by hashed area 610) stored in the cell by increasing the design variable to x_(i)*+Vx corresponds to the internal energy (represented by hashed area 620) stored in the cell for undergoing excessive strain at the current density variable x_(i)*. All design variables of the cells in the structure is updated in this manner to reduce the maximum strain to a target strain E*.

The adjustment of the design variable x_(i) may be performed in the step 530 of updating the material distribution. Specifically, the material distribution rule is expressed by the following equation to achieve (i) uniform distribution of the internal energy, and (ii) the strain constraint:

Δx _(i)=ω₁ Δx ₁ ^(i)+ω₂ Δx ₂ ^(i)  equation (15)

where Δx₁ ^((k))=K_(P)( S _(i) ^((k))−S*_(i) ^((k))) represents material distribution update for uniform internal energy distribution,

${\Delta \; x_{2}^{(k)}} = {K_{s}\left( \frac{ɛ^{*} - ɛ_{i}^{*}}{ɛ^{*}} \right)}$

represents material distribution update for the strain constraint, K_(s) is a constant representing a proportionality constant for strain, ω₁ and ω₂ represent weighted values represent how much weight should be given to uniform distribution of the internal energy and the strain constraint, respectfully. ε* represents a target strain value or a maximum allowable strain in the structure, and ε_(i)* represents the strain value at the present iteration.

The weighted values ω₁ and ω₂ are fixed during the iterations. If the final design of the structure is not satisfactory, however, a new design may be obtained by performing another set of iterations based on different weights ω₁ and ω₂. The user may change the weights ω₁ and ω₂ based on experience to obtain a desired final design.

Designing of Structures Fabricated by Extrusion Method

An extrusion manufacturing process is often employed as a method for fabricating a structure with reduced cost. In the extrusion manufacturing process, material is forced through a die using either a mechanical or hydraulic press. The structure fabricated by extrusion has a cross-section corresponding to the shape of the die. The structures fabricated by the extrusion process often require minimal secondary machining, which reduces the overall cost associated with fabricating the structures. The design of the structure manufactured by the extrusion process is different in that the two-dimensional cross-section of the structure must be defined instead of a three-dimensional topology of the structure.

In one embodiment, the HCA algorithm is modified to generate designs with a constant cross-section in a direction perpendicular to the direction of extrusion. FIG. 7 is a diagram illustrating a discretized three-dimensional structure 710 for fabrication by an extrusion method, according to one embodiment. The structure 710 is fabricated by extruding the material in direction ξ. In the extrusion method, the cross-section of the structure 710 must be retained along the direction of extrusion ξ. Therefore, the process of designing the structure 710 is reduced to a two dimensional problem of assigning design variables x_(i) to the cross-sectional columns A1 through D4.

FIG. 8 is a flow chart illustrating a method of designing the structure 710 to be fabricated by the extrusion method, according to one embodiment. First, the initial design of the structure 710 is loaded 810. The structure 710 is then segmented 816 into columns (for example, columns A1 through D4) along the direction of extrusion ξ. Each column A1 through D4 is then segmented 820 into multiple cells (for example, column B2 is segmented to cells 702A through 702N).

The FEA engine 230 performs 826 the structural analysis of the structure 710 applied with a load to compute the internal energy density in each cells of the structure 710. Then, the internal energy densities in the cells of a column are added to obtain 830 a total internal energy density in each column for the structure 710. Referring to FIG. 7, for example, the internal energy densities in the cells 702A through 720N is added to obtain the total internal energy density of column B2. The total internal energy densities in other columns are obtained in the same manner. The total internal energies of the columns A1 through D4 are illustrated in FIG. 9 as blocks with different patterns.

The cellular automata engine 240 then updates 836 the distribution of materials in each block A1 through D4 to evenly distribute the total internal energy density across the columns A1 through D4. The distribution of materials is performed in two-dimension.

Then the updated design is analyzed to confirm if predetermined termination criteria are satisfied 840. In one embodiment, the updated design is checked against the design in a previous iteration for differences in a parameter of the designs after updating the material distribution. If the change in the parameter is below a threshold, it is determined that the termination criteria are satisfied. If the termination criteria are satisfied, the process proceeds to output 846 the final design of the structure 710. If the termination criteria are not satisfied, the process returns to step 826.

Designing of Stamped Structures

In one embodiment, the HCA algorithm is used for designing a stamped structure with varying structural thickness. Like other examples described above, the goal of the design may be to evenly distribute the internal energy density across the stamped structure as much as possible. If the stamped structure has a low internal energy density at one portion, the thickness is reduced to increase the internal energy density at the same portion. In contrast, if the stamped structure has a high internal energy density at a portion, the thickness is increased to reduce the internal density at the same portion. The HCA algorithm adjusts the thickness profile of the stamped structure to evenly distribute the internal energy density across the stamped structure.

The method of designing the stamped structure using the HCA algorithm is essentially the same as the method described above with reference to FIG. 3. The major difference in designing the stamped structure compared to the embodiment of FIG. 3 is that the thickness (t) of the cell is used as the design variable instead of density (ρ), elastic modulus (E), yield stress (σ_(Y)) or strain-hardening modulus (E_(h)). Specifically, the thickness (t) is updated in step 330 using the following equation:

Δt _(i) ^((k)) =K _(P)(Ū _(i) ^((k)) −U* _(i) ^((k)))  equation (16)

where Δt_(i) ^((k)) is the change of thickness in ith cell at kth iteration of the HCA algorithm, K_(p) is a proportional control gain, Ū_(i) ^((k)) is the internal energy density of ith cell, and U*_(i) ^((k)) is a target internal energy density of the cell. The target internal energy density U*_(i) ^((k)) may be updated at each iteration based on the internal energy density of each cell.

Various modifications to the equations and processes may be made. For example, although-above embodiments were described mainly with reference to a design variable x_(i) having values not below 0 and not above 1, other unscaled variables having values beyond 0 and 1 may also be used. Further, actual mass of the cell may be used instead of the factional mass when performing the HCA algorithm.

Although the present invention has been described above with respect to several embodiments, various modifications can be made within the scope of the present invention. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims. 

1. A computer-implemented method of designing a structure representing a physical object, comprising: (a) segmenting an initial design of the structure into a plurality of cells; (b) computing a field state of the plurality of cells in the structure based on a finite element analysis; (c) computing a performance variable representing a property of the structure required to satisfy a performance constraint; (d) updating a design variable representing a material distribution in each cell based on the performance variable and a material distribution update rule to generate an updated design of the structure, the material distribution update rule changing the design variable of each cell to converge the field state of each cell towards a target field state of each cell; (e) repeating steps (b) through (d) responsive to the updated design not satisfying a terminating condition; and (f) outputting the updated design of the structure responsive to the updated design satisfying the terminating condition.
 2. The method of claim 1, wherein the property of the structure represented by the performance variable comprises a total mass of the structure.
 3. The method of claim 1, wherein the design variable is a material density of the cell or a thickness of the cell.
 4. The method of claim 1, wherein the finite element analysis comprises transient nonlinear finite element analysis.
 5. The method of claim 1, wherein the performance constraint is at least one of a maximum displacement constraint of a cell of the structure or a maximum reaction force at a cell of the structure responsive to a load applied to the structure.
 6. The method of claim 1, wherein the field state of the cell comprises an internal energy density of the cell responsive to applying a load to the structure, a difference between the design variable before updating and the design variable after updating proportional to a difference between an internal energy density of the cell and a target energy density of the cell.
 7. The method of claim 1, wherein computing the field state of the cell comprises weighting and adding a field state of the cell in a previous iteration.
 8. A computer-implemented method of designing a structure for fabrication by extrusion, comprising: (a) segmenting the structure into a plurality of columns in a direction of extrusion; (b) segmenting each column into a plurality of cells; (c) computing a field state of the plurality of cells in the structure based on a finite element analysis; (d) computing a field state of each column based on the field states of the cells of each column; (d) updating a design variable representing a material distribution in each column based on a material distribution update rule to generate an updated design of the structure, the material distribution update rule changing the design variable of each column to converge the field state of each column towards a target field state of each column; (e) repeating steps (c) through (d) responsive to the updated design not satisfying a terminating condition; and (f) outputting the updated design of the structure responsive to the updated design satisfying the terminating condition.
 9. The method of claim 8, wherein the design variable is an average density of material in each column of the structure.
 10. The method of claim 8, wherein the field state of the column comprises an aggregate of the internal energy densities of the cells in the column, a difference between the design variable before updating and the design variable after updating proportional to a difference between the aggregate internal energy density of the column and a target aggregate internal energy density of the column.
 11. A computer-implemented method of designing a shell structure representing a physical object of uneven thickness, comprising: (a) segmenting an initial design of the structure into a plurality of cells; (b) obtaining a field state of the plurality of cells in the structure based on a finite element analysis; (c) updating a thickness of each cell based on a material distribution update rule to generate an updated thickness profile of the structure, the material distribution update rule changing the thickness of each cell to converge the field state of each column towards a target field state of each column; (d) repeating steps (b) through (c) responsive to the updated thickness profile not satisfying a terminating condition; and (e) outputting the updated thickness profile of the structure responsive to the updated design satisfying the terminating condition.
 12. The method of claim 11, wherein the field state of the cell comprises an internal energy density, a thickness difference for the update and after the update proportional to a difference between the internal energy density and a target energy density.
 13. A structure designing device for generating a design of a structure representing a physical object, comprising: a cell divider configured to segment an initial design of the structure into a plurality of cells; a finite element analysis engine configured to compute field states of the plurality of cells of a design of the structure in a current iteration by a finite element analysis; and a cellular automata engine configured to compute a performance variable representing a property of the structure required to satisfy a performance constraint in the current iteration, the cellular automata engine configured to update a design variable representing a material distribution in each cell based on the performance variable and a material distribution update rule to generate an updated design of the structure for a next iteration subsequent to the current iteration, the material distribution update rule changing the design variable of each cell to converge the field state of each cell towards a target field state of each cell over a plurality of iterations, the updated design of the structure provided to the finite element analysis engine to compute a field state in the next iteration, cellular automata engine configured to output the updated design of the structure responsive to the updated design satisfying a terminating condition.
 14. The structure designing device of claim 13, wherein the property of the structure represented by the performance variable comprises a total mass of the structure.
 15. The structure designing device of claim 13, wherein the design variable is a material density of the cell or a thickness of the cell.
 16. The structure designing device of claim 13, wherein the finite element analysis comprises a transient nonlinear finite element analysis.
 17. The structure designing device of claim 13, wherein the performance constraint is at least one of a maximum displacement constraint of a cell of the structure or a maximum reaction force at a cell of the structure responsive to a load applied to the structure.
 18. A structure designing device for generating a design of a structure representing a physical object for fabrication by extrusion, comprising: a cell divider configured to segment the structure into a plurality of columns in a direction of extrusion, the cell divider further configured to segment each column into a plurality of cells; a finite element analysis engine configured to compute field states of the plurality of cells in the structure in a current iteration based on a finite element analysis, the finite element analysis engine further configured to obtain a field state of a column based on the field states of cells of the column; a cellular automata engine configured to update a design variable representing a material distribution in each column based on a material distribution update rule to generate an updated design of the structure for a next iteration subsequent to the current iteration, the material distribution update rule changing the design variable of each column to converge the field state of each column towards a target field state of each column, the updated design of the structure provided to the finite element analysis engine to compute a field state in the next iteration, the cellular automata engine configured to output the updated design of the structure responsive to the updated design satisfying a terminating condition.
 19. The structure designing device of claim 18, wherein the design variable is a density of material.
 20. The structure designing device of claim 18, wherein the field state of the column comprises an aggregate of the internal energy density of the cells in the column, a difference between the design variable before updating and the design variable after updating proportional to a difference between the aggregate internal energy density and a target aggregate internal energy density.
 21. A structure designing device for generating a design of a shell structure representing a physical object of uneven thickness, comprising: a cell divider configured to segment an initial profile of the structure into a plurality of cells; a finite element analysis engine configured to obtain a field state of the plurality of cells of the structure in a current iteration based on a finite element analysis; and a cellular automata engine configured to update thickness of each cell based on a material distribution update rule to generate an updated thickness profile of the structure for a next iteration subsequent to the current iteration, the material distribution update rule changing the thickness of each cell to converge the field state of each cell towards a target field state of each cell, the updated thickness profile of the structure provided to the finite element analysis engine to compute a field state in the next iteration, the cellular automata engine configured to output the updated thickness profile of the structure responsive to the updated design satisfying a terminating condition.
 22. The structure designing device of claim 21, wherein the field state of the cell comprises an internal energy density, a thickness difference before updating and after updating proportional to a difference between the internal energy density and a target energy density.
 23. A computer readable storage medium storing instructions thereon for designing a structure representing a physical object, the instructions when executed by a processor cause the processor to: (a) segment an initial design of the structure into a plurality of cells; (b) compute a field state of the plurality of cells in the structure based on a finite element analysis; (c) compute a performance variable representing a property of the structure required to satisfy a performance constraint; (d) update a design variable representing a material distribution in each cell based on the performance variable and a material distribution update rule to generate an updated design of the structure, the material distribution update rule changing the design variable of each cell to converge the field state of each cell towards a target field state of each cell; (e) repeat instructions causing (b) through (d) responsive to the updated design not satisfying a terminating condition; and (f) output the updated design of the structure responsive to the updated design satisfying the terminating condition.
 24. The computer readable storage medium of claim 23, wherein the property of the structure represented by the performance variable comprises a total mass of the structure.
 25. The computer readable storage medium of claim 23, wherein the design variable is a material density of the cell or a thickness of the cell.
 26. The computer readable storage medium of claim 23, wherein the finite element analysis comprises transient nonlinear finite element analysis.
 27. The computer readable storage medium of claim 23, wherein the performance constraint is at least one of a maximum displacement constraint of a cell of the structure or a maximum reaction force at a cell of the structure responsive to a load applied to the structure.
 28. The computer readable storage medium of claim 23, wherein the field state of the cell comprises an internal energy density of the cell responsive to applying a load to the structure, a difference between the design variable before updating and the design variable after updating proportional to a difference between an internal energy density of the cell and a target energy density of the cell.
 29. The computer readable storage medium of claim 23, wherein instruction to compute the field state of the cell comprises instructions to weight and add a field state of the cell in a previous iteration.
 30. A computer readable storage medium storing instructions thereon for designing a structure for fabrication by extrusion, the instructions when executed by a processor cause the processor to: (a) segment the structure into a plurality of columns in a direction of extrusion; (b) segment each column into a plurality of cells; (c) obtain a field state of cells in the structure based on a finite element analysis; (d) obtain the field state of a column based on the field states of cells in the column; (d) update a design variable representing a material distribution in each column based on a material distribution update rule to generate an updated design of the structure, the material distribution update rule changing the design variable of each column to converge the field state of each column towards a target field state of each column; (e) repeat instructions for (c) through (d) responsive to the updated design not satisfying a terminating condition; and (f) output the updated design of the structure responsive to the updated design satisfying the terminating condition.
 31. The computer readable storage medium of claim 30, wherein the design variable is a density of material in a column of the structure.
 32. The computer readable storage medium of claim 30, wherein the field state of the column comprises an aggregate of the internal energy density of the cells in the column, a difference between the design variable before updating and the design variable after updating the design variable proportional to a difference between the aggregate internal energy density and a target aggregate internal energy density.
 33. A computer readable storage medium storing instructions thereon for designing a shell structure representing a physical object of uneven thickness, the instructions when executed by a processor cause the processor to: (a) segment an initial design of the structure into a plurality of cells; (b) obtain a field state of a cell in the structure based on a finite element analysis; (c) update a thickness of each cell based on a material distribution update rule to generate an updated thickness profile of the structure, the material distribution update rule changing the thickness of each cell to converge the field state of each column towards a target field state of each column; (d) repeat instructions for (b) through (c) responsive to the updated thickness provide not satisfying the terminating condition; and (e) output the updated thickness profile of the structure responsive to the updated design satisfying the terminating condition.
 34. The computer readable storage medium of claim 33, wherein the field state of the cell comprises an internal energy density, a difference before updating the thickness update and after updating the thickness proportional to a difference between the internal energy density and a target energy density. 